Wood defect detection is critical for ensuring quality control in the wood processing industry. However, current industrial applications face two major challenges: traditional methods are costly, subjective, and labor-intensive, while mainstream deep learning models often struggle to balance detection accuracy and computational efficiency for edge deployment. To address these issues, this study proposes CFIS-YOLO, a lightweight object detection model optimized for edge devices. The model introduces an enhanced C2f structure, a dynamic feature recombination module, and a novel loss function that incorporates auxiliary bounding boxes and angular constraints. These innovations improve multi-scale feature fusion and small object localization while significantly reducing computational overhead. Evaluated on a public wood defect dataset, CFIS-YOLO achieves a mean Average Precision (mAP@0.5) of 77.5\%, outperforming the baseline YOLOv10s by 4 percentage points. On SOPHON BM1684X edge devices, CFIS-YOLO delivers 135 FPS, reduces power consumption to 17.3\% of the original implementation, and incurs only a 0.5 percentage point drop in mAP. These results demonstrate that CFIS-YOLO is a practical and effective solution for real-world wood defect detection in resource-constrained environments.

本研究解决了木材缺陷检测中传统方法成本高、主观性强及深度学习模型在边缘部署时难以平衡检测准确性与计算效率的难题。通过提出CFIS-YOLO模型，该模型引入了增强的C2f结构、动态特征重组合模块及新颖的损失函数，显著改善了多尺度特征融合及小物体定位，并在公共木材缺陷数据集上实现了77.5%的mAP，显示了其在资源受限环境中进行木材缺陷检测的实际有效性。

CFIS-YOLO：一种轻量级多尺度融合网络用于边缘可部署的木材缺陷检测